ImageModelDistributionSettingsObjectDetection Class
Definition
Important
Some information relates to prerelease product that may be substantially modified before it’s released. Microsoft makes no warranties, express or implied, with respect to the information provided here.
Distribution expressions to sweep over values of model settings. <example> Some examples are:
ModelName = "choice('seresnext', 'resnest50')";
LearningRate = "uniform(0.001, 0.01)";
LayersToFreeze = "choice(0, 2)";
```</example>
For more details on how to compose distribution expressions please check the documentation:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters
For more information on the available settings please visit the official documentation:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
public class ImageModelDistributionSettingsObjectDetection : Azure.ResourceManager.MachineLearning.Models.ImageModelDistributionSettings, System.ClientModel.Primitives.IJsonModel<Azure.ResourceManager.MachineLearning.Models.ImageModelDistributionSettingsObjectDetection>, System.ClientModel.Primitives.IPersistableModel<Azure.ResourceManager.MachineLearning.Models.ImageModelDistributionSettingsObjectDetection>public class ImageModelDistributionSettingsObjectDetection : Azure.ResourceManager.MachineLearning.Models.ImageModelDistributionSettingstype ImageModelDistributionSettingsObjectDetection = class
inherit ImageModelDistributionSettings
interface IJsonModel<ImageModelDistributionSettingsObjectDetection>
interface IPersistableModel<ImageModelDistributionSettingsObjectDetection>type ImageModelDistributionSettingsObjectDetection = class
inherit ImageModelDistributionSettingsPublic Class ImageModelDistributionSettingsObjectDetection
Inherits ImageModelDistributionSettings
Implements IJsonModel(Of ImageModelDistributionSettingsObjectDetection), IPersistableModel(Of ImageModelDistributionSettingsObjectDetection)Public Class ImageModelDistributionSettingsObjectDetection
Inherits ImageModelDistributionSettings- Inheritance
- Implements
Constructors
| ImageModelDistributionSettingsObjectDetection() |
Initializes a new instance of ImageModelDistributionSettingsObjectDetection. |
Properties
| AmsGradient |
Enable AMSGrad when optimizer is 'adam' or 'adamw'. (Inherited from ImageModelDistributionSettings) |
| Augmentations |
Settings for using Augmentations. (Inherited from ImageModelDistributionSettings) |
| Beta1 |
Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. (Inherited from ImageModelDistributionSettings) |
| Beta2 |
Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. (Inherited from ImageModelDistributionSettings) |
| BoxDetectionsPerImage |
Maximum number of detections per image, for all classes. Must be a positive integer. Note: This settings is not supported for the 'yolov5' algorithm. |
| BoxScoreThreshold |
During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1]. |
| Distributed |
Whether to use distributer training. (Inherited from ImageModelDistributionSettings) |
| EarlyStopping |
Enable early stopping logic during training. (Inherited from ImageModelDistributionSettings) |
| EarlyStoppingDelay |
Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer. (Inherited from ImageModelDistributionSettings) |
| EarlyStoppingPatience |
Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer. (Inherited from ImageModelDistributionSettings) |
| EnableOnnxNormalization |
Enable normalization when exporting ONNX model. (Inherited from ImageModelDistributionSettings) |
| EvaluationFrequency |
Frequency to evaluate validation dataset to get metric scores. Must be a positive integer. (Inherited from ImageModelDistributionSettings) |
| GradientAccumulationStep |
Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer. (Inherited from ImageModelDistributionSettings) |
| ImageSize |
Image size for train and validation. Must be a positive integer. Note: The training run may get into CUDA OOM if the size is too big. Note: This settings is only supported for the 'yolov5' algorithm. |
| LayersToFreeze |
Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models. (Inherited from ImageModelDistributionSettings) |
| LearningRate |
Initial learning rate. Must be a float in the range [0, 1]. (Inherited from ImageModelDistributionSettings) |
| LearningRateScheduler |
Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'. (Inherited from ImageModelDistributionSettings) |
| MaxSize |
Maximum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm. |
| MinSize |
Minimum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm. |
| ModelName |
Name of the model to use for training. For more information on the available models please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models. (Inherited from ImageModelDistributionSettings) |
| ModelSize |
Model size. Must be 'small', 'medium', 'large', or 'xlarge'. Note: training run may get into CUDA OOM if the model size is too big. Note: This settings is only supported for the 'yolov5' algorithm. |
| Momentum |
Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1]. (Inherited from ImageModelDistributionSettings) |
| MultiScale |
Enable multi-scale image by varying image size by +/- 50%. Note: training run may get into CUDA OOM if no sufficient GPU memory. Note: This settings is only supported for the 'yolov5' algorithm. |
| Nesterov |
Enable nesterov when optimizer is 'sgd'. (Inherited from ImageModelDistributionSettings) |
| NmsIouThreshold |
IOU threshold used during inference in NMS post processing. Must be float in the range [0, 1]. |
| NumberOfEpochs |
Number of training epochs. Must be a positive integer. (Inherited from ImageModelDistributionSettings) |
| NumberOfWorkers |
Number of data loader workers. Must be a non-negative integer. (Inherited from ImageModelDistributionSettings) |
| Optimizer |
Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'. (Inherited from ImageModelDistributionSettings) |
| RandomSeed |
Random seed to be used when using deterministic training. (Inherited from ImageModelDistributionSettings) |
| StepLRGamma |
Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1]. (Inherited from ImageModelDistributionSettings) |
| StepLRStepSize |
Value of step size when learning rate scheduler is 'step'. Must be a positive integer. (Inherited from ImageModelDistributionSettings) |
| TileGridSize |
The grid size to use for tiling each image. Note: TileGridSize must not be None to enable small object detection logic. A string containing two integers in mxn format. Note: This settings is not supported for the 'yolov5' algorithm. |
| TileOverlapRatio |
Overlap ratio between adjacent tiles in each dimension. Must be float in the range [0, 1). Note: This settings is not supported for the 'yolov5' algorithm. |
| TilePredictionsNmsThreshold |
The IOU threshold to use to perform NMS while merging predictions from tiles and image. Used in validation/ inference. Must be float in the range [0, 1]. Note: This settings is not supported for the 'yolov5' algorithm. NMS: Non-maximum suppression |
| TrainingBatchSize |
Training batch size. Must be a positive integer. (Inherited from ImageModelDistributionSettings) |
| ValidationBatchSize |
Validation batch size. Must be a positive integer. (Inherited from ImageModelDistributionSettings) |
| ValidationIouThreshold |
IOU threshold to use when computing validation metric. Must be float in the range [0, 1]. |
| ValidationMetricType |
Metric computation method to use for validation metrics. Must be 'none', 'coco', 'voc', or 'coco_voc'. |
| WarmupCosineLRCycles |
Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1]. (Inherited from ImageModelDistributionSettings) |
| WarmupCosineLRWarmupEpochs |
Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer. (Inherited from ImageModelDistributionSettings) |
| WeightDecay |
Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1]. (Inherited from ImageModelDistributionSettings) |
